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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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14274154 · Jun 202019922001200920172026
48 results for Hessian compatibility

Curved Frobenius manifolds link to Hessian metrics in geometry.

problem Understanding curved Frobenius manifolds and their relation to Hessian metrics.
method Analyzing the relationship between curved Frobenius structures and Hessian metrics on spaces with non-vanishing curvature.
result Consistent curved Frobenius structures on constant curvature spaces are linked to Hessian metrics.

Study on Hermitian Calabi functional in complexified orbits of symplectic manifolds.

problem Analyzing the Hermitian Calabi functional on complexified orbits of symplectic manifolds.
method Explicit formula for Hessian of Hermitian Calabi functional, semi-positive definiteness proof, and weak parabolicity of Hermitian Calabi flow.
result Hessian of Hermitian Calabi functional is semi-positive definite on complexified orbits.

We analyze the Hessian spectra of large models up to 100B parameters.

problem Accurate Hessian spectra of large foundation models are difficult to obtain.
method We use shard-local finite-difference Hessian vector products and stochastic Lanczos quadrature.
result We produce the first large-scale spectral density estimates of foundation models.

Improved online learning for hidden-convex losses achieves optimal regret.

problem Adversarial online learning with nonconvex losses that become convex after reparameterization.
method Algorithmic equivalence between OGD and OMD on convex losses, with Hessian compatibility condition.
result OGD achieves O(T)\mathcal{O}(\sqrt{T}) regret for hidden-convex losses, matching optimal rate.

Affine manifolds linked to integrable equations and geometric structures.

problem Understanding the geometric and algebraic properties of affine manifolds.
method Analyzing the Kahlerian tangent bundle and multi-dimensional consistency of the TED equation.
result Affine manifolds are related to self-dual Einstein spaces and Hessian structures.

The paper introduces new structures for left-symmetric algebroids.

problem Developing new mathematical structures for left-symmetric algebroids.
method Introducing Koszul-Vinberg-Nijenhuis structures and related concepts.
result Koszul-Vinberg-Nijenhuis structures provide a hierarchy of structures.

New proof shows affine manifolds with parallel volume are Riemannian-flat.

problem Characterize compact affine manifolds with parallel volume.
method Construct a representative metric with Levi-Civita connection, using Hessian of volume-normalized distance functions.
result Affine manifolds with parallel volume are Riemannian-flat.

New superintegrable systems derived from Frobenius structures.

problem Constructing second-order superintegrable systems.
method Using conification and direct product construction, applying to semi-simple and nilpotent algebras.
result Explicitly constructed second-order superintegrable systems in three dimensions.

Lagrange geometry is the geometry of the tensor field defined by the fiberwise Hessian of a non degenerate Lagrangian function on the total space of a tangent bundle. Finsler geometry is the geometrically most interesting case of Lagrange geometry. In this paper we study a generalization, which consists of replacing th…

2002-12-05abs ↗pdf ↗

For the purpose of understanding second-order scalar PDEs and their hydrodynamic integrability, we introduce G-structures that are induced on hypersurfaces of the space of symmetric matrices (interpreted as the fiber of second-order jet space) and are defined by non-degenerate scalar second-order-only (Hessian) PDEs in…

2010-10-28abs ↗pdf ↗

Study norm-squared of momentum map in infinite dimensions with applications to symplectic geometry.

problem Understanding the norm-squared of the momentum map in infinite-dimensional settings.
method Calculation of Hessian, decomposition of stabilizer, application to symplectic and complex structures.
result Positive semi-definiteness of Hessian along complexified orbit and new central extensions of symplectomorphism group.

The study proves that certain noncompact Hessian manifolds are diffeomorphic to R^n.

problem Characterizing complete noncompact Hessian manifolds with nonnegative Hessian sectional curvature.
method Using a geometric flow on noncompact affine Riemannian manifolds, constructing Hessian metrics, and proving diffeomorphism.
result Complete noncompact Hessian manifolds with nonnegative Hessian sectional curvature are diffeomorphic to R^n if their tangent bundle has maximal volume growth.

New Hessian estimates for heat equations on manifolds.

problem Estimating Hessian matrices for heat-type equations on Riemannian manifolds.
method Using Bismut-Stroock Hessian formula, with explicit coefficients and delay/growth rate functions.
result Novel backward weak Harnack inequality and precise pointwise Hessian estimates for eigenfunctions.

We prove that, in dimensions greater than 2, the generic metric is not a Hessian metric and find a curvature condition on Hessian metrics in dimensions greater than 3. In particular we prove that the forms used to define the Pontryagin classes in terms of the curvature vanish on a Hessian manifold. By contrast all anal…

2013-12-04abs ↗pdf ↗

Criterion for solvability of complex 2-Hessian equation on compact Kähler manifolds.

problem Solvability of complex 2-Hessian equation on compact Kähler manifolds.
method Nakai--Moishezon-type criterion associated with the complex 2-Hessian equation.
result Criterion equivalent to existence of a smooth 2-admissible representative in complex dimension three.

Constructs homogeneous Kähler structures on tangent bundles of Hessian manifolds.

problem Creating Kähler structures on tangent bundles of Hessian manifolds.
method Endowing Hessian manifolds with Kähler structures using group actions and homothetic vector fields.
result Homogeneous conformally Kähler structures on tangent bundles of selfsimilar Hessian manifolds.

Paper proves inequalities on Hermitian manifolds with applications to bounded solutions.

problem Establishing mixed Hessian inequalities on Hermitian manifolds.
method Weak convergence theorem of complex Hessian operators and general mixed Hessian inequality.
result Existence of bounded solutions of complex Hessian equations.

A selfsimiar manifold is a Riemannian manifold (M,g)\left(M,g\right) endowed with a homothetic vector field ξξ. We characterize global selfsimilar manifolds and describe the structure of local selfsimilar manifolds. We prove that any selfsimilar manifold with a potential homothetic vector field is a conical Riemannian ma…

2019-08-05abs ↗pdf ↗

This paper uncovers the low-rank structure of neural network Hessians.

problem Understanding the structure of Hessians in neural networks.
method Proposes a decoupling conjecture to decompose layer-wise Hessians into Kronecker products of smaller matrices.
result Proves the structure of top eigenspaces in 2-layer networks and shows high overlap in top eigenvectors across different models.

Study confirms Chern's conjecture on compact Hessian manifolds and classifies their topologies.

problem Global topological constraints and structural properties of compact Hessian manifolds.
method Novel fibration and splitting theorems, Chern's conjecture, Hitchin systems, Cheng-Yau solution.
result Topological classification of complete Hessian surfaces and closed orientable Hessian 3-manifolds.

Paper discusses solving generalized Hessian inequalities with various operators.

problem Finding global solutions to generalized Hessian inequalities.
method Analyzes various Hessian operators and provides conditions for global solvability.
result Provides necessary and sufficient conditions for global solvability of generalized Hessian inequalities.

Hessian alignment improves OOD generalization in deep learning.

problem Improving deep learning models' ability to generalize to out-of-distribution data.
method Analyzed Hessian and gradient alignment for domain generalization using recent OOD theory.
result Hessian alignment methods achieve promising performance on various OOD benchmarks.

New perspective on CNNs using Hessian maps reveals their structure.

problem Understanding the nature of Convolutional Neural Networks (CNNs).
method Developed a framework using Toeplitz representation of CNNs to reveal Hessian structure and prove rank bounds.
result Proved that the Hessian rank of CNNs grows as the square root of the number of parameters.

Study the Hessian geometry of an ideal gas in a centrifuge.

problem Understanding the Hessian geometry of an ideal gas in a centrifuge.
method Investigate the Hessian geometry associated with an ideal gas in a spherical centrifuge, using the action of the Euclidean rotation group.
result The Hessian geometry of a spherical rigid body is isometric to a hyperbolic space in the high angular velocity limit.

New Hessian estimators for Riemannian manifolds with reduced bias.

problem Estimating Hessians on Riemannian manifolds with reduced bias and computational efficiency.
method Introducing new stochastic zeroth-order Hessian estimators using O(1)O(1) function evaluations.
result Achieved a bias bound of order O(γδ2)O(γδ^2) for analytic real-valued functions.

New algorithm adds Hessian regularization to improve neural network robustness.

problem Improving neural network robustness against adversarial attacks.
method Proposes an efficient algorithm to train neural networks with Hessian operator-norm regularization.
result Hessian operator-norm regularization increases neural network robustness over input gradient regularization.